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        <h1 id="绘制特征选择结果图"><a href="#绘制特征选择结果图" class="headerlink" title="绘制特征选择结果图"></a>绘制特征选择结果图</h1><ol>
<li>利用RFE-RF生成的数据特征准确率数据去判定最佳的特征数目</li>
<li>比较不同组合下的特征之间的数目和区别</li>
</ol>
<h2 id="slpdb库中的全部数据下的特征选择结果图"><a href="#slpdb库中的全部数据下的特征选择结果图" class="headerlink" title="slpdb库中的全部数据下的特征选择结果图"></a>slpdb库中的全部数据下的特征选择结果图</h2><p>先读取出slpdb中2期的分期准确率结果</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">3</span>):</span><br><span class="line">    data = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_acr_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    df = data.T</span><br><span class="line">    <span class="comment"># 读取18个数据的平均准确率</span></span><br><span class="line">    F_mean = []</span><br><span class="line">    <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>):</span><br><span class="line">        <span class="comment"># 读取25个特征子集组合的特征平均准确率</span></span><br><span class="line">        feature_num_mean = []</span><br><span class="line">        <span class="keyword">for</span> num <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>):</span><br><span class="line">            <span class="comment"># 读取第一个数据的前面准确率结果</span></span><br><span class="line">            <span class="comment"># 特征对应的准确率</span></span><br><span class="line">            feature_acr = []</span><br><span class="line">            <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>):</span><br><span class="line">                feature_acr.append(<span class="built_in">eval</span>(df[k][i])[num])</span><br><span class="line">            <span class="comment"># 将50准确率求平均值</span></span><br><span class="line">            feature_mean = np.array(feature_acr).mean()</span><br><span class="line">            feature_num_mean.append(feature_mean)</span><br><span class="line">        F_mean.append(feature_num_mean)</span><br></pre></td></tr></table></figure>

<p>进行化简，F_mean暂时留着吧，也没啥用，到时直接读取数据的量就行了去针对性的读取，还是留着吧</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">3</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_acr_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>).T</span><br><span class="line">    <span class="comment"># 读取18个数据的平均准确率</span></span><br><span class="line">    F_mean = [[np.array([<span class="built_in">eval</span>(df[k][i])[num] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)]).mean() <span class="keyword">for</span> num <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)] <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)]</span><br><span class="line">    <span class="comment"># 对应特征求一个平均准确率，先用整体的来计算</span></span><br><span class="line">    <span class="comment"># 总共有25个特征组合</span></span><br><span class="line">    feature_total = []</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>):</span><br><span class="line">        acr = []</span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(F_mean)):</span><br><span class="line">            acr.append(F_mean[i][j])</span><br><span class="line">        <span class="comment"># 转换为百分数，乘以100</span></span><br><span class="line">        all_mean = np.array(acr).mean()*<span class="number">100</span></span><br><span class="line">        feature_total.append(all_mean)</span><br></pre></td></tr></table></figure>

<p>这儿对应的25个特征组合，在18个数据下的平均准确率</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/23</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将2345期的组合放到1个list中</span></span><br><span class="line">slpdb_stage = []</span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">6</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_acr_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>).T</span><br><span class="line">    <span class="comment"># 读取18个数据的平均准确率</span></span><br><span class="line">    F_mean = [[np.array([<span class="built_in">eval</span>(df[k][i])[num] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)]).mean() <span class="keyword">for</span> num <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)] <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)]</span><br><span class="line">    <span class="comment"># 总共有25个特征组合</span></span><br><span class="line">    feature_total = [np.array([F_mean[i][j] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(F_mean))]).mean()*<span class="number">100</span> <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)]</span><br><span class="line">    slpdb_stage.append(feature_total)</span><br></pre></td></tr></table></figure>

<h2 id="slpdb全部数据的绘制"><a href="#slpdb全部数据的绘制" class="headerlink" title="slpdb全部数据的绘制"></a>slpdb全部数据的绘制</h2><p>将其进行绘制</p>
<p> <a target="_blank" rel="noopener" href="https://blog.csdn.net/Poul_henry/article/details/82590392?utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2~all~first_rank_v2~rank_v25-1-82590392.nonecase&utm_term=python%E6%94%B9%E6%A8%AA%E5%9D%90%E6%A0%87">https://blog.csdn.net/Poul_henry/article/details/82590392?utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2<del>all</del>first_rank_v2~rank_v25-1-82590392.nonecase&amp;utm_term=python%E6%94%B9%E6%A8%AA%E5%9D%90%E6%A0%87</a> </p>
<p>横纵坐标的更改</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将2345期的组合放到1个list中</span></span><br><span class="line">slpdb_stage = []</span><br><span class="line"><span class="comment"># 读取2345期的睡眠结果</span></span><br><span class="line"><span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">2</span>, <span class="number">6</span>):</span><br><span class="line">    df = pd.read_excel(<span class="string">&#x27;E:/8-23 feature section and importance/slpdb_feature_acr_stage&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % index + <span class="string">&#x27;.xlsx&#x27;</span>).T</span><br><span class="line">    <span class="comment"># 读取18个数据的平均准确率</span></span><br><span class="line">    F_mean = [[np.array([<span class="built_in">eval</span>(df[k][i])[num] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">50</span>)]).mean() <span class="keyword">for</span> num <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)] <span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">18</span>)]</span><br><span class="line">    <span class="comment"># 总共有25个特征组合</span></span><br><span class="line">    feature_total = [np.array([F_mean[i][j] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(F_mean))]).mean()*<span class="number">100</span> <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)]</span><br><span class="line">    slpdb_stage.append(feature_total)</span><br><span class="line"></span><br><span class="line">x = np.arange(<span class="number">1</span>, <span class="number">26</span>)</span><br><span class="line">x1 = np.arange(<span class="number">5</span>, <span class="number">31</span>)</span><br><span class="line"></span><br><span class="line">fig = plt.figure(figsize=(<span class="number">10</span>, <span class="number">8</span>))</span><br><span class="line"><span class="comment"># 显示中文标签</span></span><br><span class="line">plt.rcParams[<span class="string">&#x27;font.sans-serif&#x27;</span>] = [<span class="string">&#x27;SimHei&#x27;</span>]</span><br><span class="line"><span class="comment"># x轴重命名</span></span><br><span class="line">plt.xticks(x, x1)</span><br><span class="line">plt.plot(x, slpdb_stage[<span class="number">0</span>], <span class="string">&quot;k*--&quot;</span>, linewidth=<span class="number">1</span>, label=<span class="string">&#x27;class_2&#x27;</span>)</span><br><span class="line">plt.plot(x, slpdb_stage[<span class="number">1</span>], <span class="string">&quot;b*--&quot;</span>, linewidth=<span class="number">1</span>, label=<span class="string">&#x27;class_3&#x27;</span>)</span><br><span class="line">plt.plot(x, slpdb_stage[<span class="number">2</span>], <span class="string">&quot;r*--&quot;</span>, linewidth=<span class="number">1</span>, label=<span class="string">&#x27;class_4&#x27;</span>)</span><br><span class="line">plt.plot(x, slpdb_stage[<span class="number">3</span>], <span class="string">&quot;y*--&quot;</span>, linewidth=<span class="number">1</span>, label=<span class="string">&#x27;class_4&#x27;</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">0</span>, <span class="number">4</span>):</span><br><span class="line">    plt.scatter(x[slpdb_stage[i].index(<span class="built_in">max</span>(slpdb_stage[i]))], <span class="built_in">max</span>(slpdb_stage[i]), s=<span class="number">80</span>, color=<span class="string">&#x27;r&#x27;</span>)</span><br><span class="line"></span><br><span class="line">plt.xlabel(<span class="string">&quot;Number of Features&quot;</span>, fontsize=<span class="number">10</span>)</span><br><span class="line">plt.xticks(fontsize=<span class="number">8</span>)</span><br><span class="line">plt.ylabel(<span class="string">&quot;Average Accuracy of RF/%&quot;</span>, fontsize=<span class="number">10</span>)</span><br><span class="line">plt.yticks(fontsize=<span class="number">8</span>)</span><br><span class="line">plt.legend(loc=<span class="string">&#x27;best&#x27;</span>)</span><br><span class="line"><span class="comment"># 隐藏左边和上边的边框</span></span><br><span class="line">ax = plt.gca()</span><br><span class="line">ax.spines[<span class="string">&#x27;right&#x27;</span>].set_color(<span class="string">&#x27;none&#x27;</span>)</span><br><span class="line">ax.spines[<span class="string">&#x27;top&#x27;</span>].set_color(<span class="string">&#x27;none&#x27;</span>)</span><br><span class="line">plt.show()</span><br><span class="line"></span><br><span class="line">fig.savefig(<span class="string">&#x27;slpdb所有数据的一起绘制图.png&#x27;</span>, dpi=<span class="number">1600</span>, bbox_inches=<span class="string">&#x27;tight&#x27;</span>)</span><br></pre></td></tr></table></figure>

<p><img src="H:\myboke\mybike\source\images\slpdb所有数据的一起绘制图.png"></p>
<p>太模糊了将其绘制成子图的形式</p>
<h2 id="子图形式的绘制"><a href="#子图形式的绘制" class="headerlink" title="子图形式的绘制"></a>子图形式的绘制</h2><p><img src="H:\myboke\mybike\source\images\slpdb数据的子图绘制.png"></p>
<h2 id="特征数目表"><a href="#特征数目表" class="headerlink" title="特征数目表"></a>特征数目表</h2><table>
<thead>
<tr>
<th>特征数</th>
<th><strong>stage_2</strong></th>
<th><strong>stage_3</strong></th>
<th><strong>stage_4</strong></th>
<th><strong>stage_5</strong></th>
</tr>
</thead>
<tbody><tr>
<td><strong>5</strong></td>
<td>88.91</td>
<td>86.38</td>
<td>83.23</td>
<td>76.15</td>
</tr>
<tr>
<td><strong>6</strong></td>
<td>89.21</td>
<td>86.99</td>
<td>83.86</td>
<td>76.82</td>
</tr>
<tr>
<td><strong>7</strong></td>
<td>89.55</td>
<td>87.23</td>
<td>84.21</td>
<td>77.23</td>
</tr>
<tr>
<td><strong>8</strong></td>
<td>89.82</td>
<td>87.62</td>
<td>84.49</td>
<td>77.76</td>
</tr>
<tr>
<td><strong>9</strong></td>
<td>90.06</td>
<td>87.93</td>
<td>85.03</td>
<td>77.96</td>
</tr>
<tr>
<td><strong>10</strong></td>
<td>90.04</td>
<td>88.19</td>
<td>85.22</td>
<td>78.14</td>
</tr>
<tr>
<td><strong>11</strong></td>
<td>90.12</td>
<td>88.27</td>
<td>85.36</td>
<td>78.23</td>
</tr>
<tr>
<td><strong>12</strong></td>
<td>90.29</td>
<td>88.29</td>
<td>85.59</td>
<td>78.77</td>
</tr>
<tr>
<td><strong>13</strong></td>
<td>90.42</td>
<td>88.55</td>
<td>85.62</td>
<td>78.68</td>
</tr>
<tr>
<td><strong>14</strong></td>
<td>90.46</td>
<td>88.57</td>
<td>85.75</td>
<td>78.81</td>
</tr>
<tr>
<td><strong>15</strong></td>
<td>90.53</td>
<td>88.64</td>
<td>85.86</td>
<td>78.91</td>
</tr>
<tr>
<td><strong>16</strong></td>
<td>90.64</td>
<td>88.70</td>
<td>85.95</td>
<td>78.99</td>
</tr>
<tr>
<td><strong>17</strong></td>
<td>90.59</td>
<td>88.74</td>
<td>86.06</td>
<td>79.11</td>
</tr>
<tr>
<td><strong>18</strong></td>
<td>90.63</td>
<td>88.80</td>
<td>86.07</td>
<td>79.16</td>
</tr>
<tr>
<td><strong>19</strong></td>
<td>90.68</td>
<td>88.86</td>
<td>86.07</td>
<td>79.15</td>
</tr>
<tr>
<td><strong>20</strong></td>
<td>90.59</td>
<td>88.79</td>
<td>86.09</td>
<td>79.32</td>
</tr>
<tr>
<td><strong>21</strong></td>
<td>90.71</td>
<td>88.91</td>
<td>86.13</td>
<td>79.39</td>
</tr>
<tr>
<td><strong>22</strong></td>
<td>90.76</td>
<td>88.87</td>
<td>86.12</td>
<td>79.26</td>
</tr>
<tr>
<td><strong>23</strong></td>
<td>90.70</td>
<td>88.92</td>
<td>86.31</td>
<td>79.30</td>
</tr>
<tr>
<td><strong>24</strong></td>
<td>90.76</td>
<td>88.88</td>
<td>86.26</td>
<td>79.39</td>
</tr>
<tr>
<td><strong>25</strong></td>
<td>90.72</td>
<td>89.00</td>
<td>86.26</td>
<td>79.54</td>
</tr>
<tr>
<td><strong>26</strong></td>
<td>90.73</td>
<td>88.93</td>
<td>86.18</td>
<td>79.41</td>
</tr>
<tr>
<td><strong>27</strong></td>
<td>90.73</td>
<td>88.96</td>
<td>86.24</td>
<td>79.44</td>
</tr>
<tr>
<td><strong>28</strong></td>
<td>90.73</td>
<td>89.05</td>
<td>86.27</td>
<td>79.54</td>
</tr>
<tr>
<td><strong>29</strong></td>
<td>90.67</td>
<td>88.95</td>
<td>86.28</td>
<td>79.65</td>
</tr>
</tbody></table>
<h2 id="ucddb库中的全部数据的绘制结果"><a href="#ucddb库中的全部数据的绘制结果" class="headerlink" title="ucddb库中的全部数据的绘制结果"></a>ucddb库中的全部数据的绘制结果</h2><p><img src="H:\myboke\mybike\source\images\ucddb所有数据的一起绘制图.png"></p>
<h2 id="子图形式的绘制-1"><a href="#子图形式的绘制-1" class="headerlink" title="子图形式的绘制"></a>子图形式的绘制</h2><p><img src="H:\myboke\mybike\source\images\ucddb数据的子图绘制.png"></p>
<h2 id="特征数目表-1"><a href="#特征数目表-1" class="headerlink" title="特征数目表"></a>特征数目表</h2><table>
<thead>
<tr>
<th align="center"></th>
<th align="center"><strong>stage_2</strong></th>
<th align="center"><strong>stage_3</strong></th>
<th align="center"><strong>stage_4</strong></th>
<th align="center"><strong>stage_5</strong></th>
</tr>
</thead>
<tbody><tr>
<td align="center"><strong>5</strong></td>
<td align="center">90.50</td>
<td align="center">86.09</td>
<td align="center">81.88</td>
<td align="center">76.48</td>
</tr>
<tr>
<td align="center"><strong>6</strong></td>
<td align="center">90.79</td>
<td align="center">86.86</td>
<td align="center">82.98</td>
<td align="center">77.87</td>
</tr>
<tr>
<td align="center"><strong>7</strong></td>
<td align="center">91.10</td>
<td align="center">87.34</td>
<td align="center">83.64</td>
<td align="center">78.77</td>
</tr>
<tr>
<td align="center"><strong>8</strong></td>
<td align="center">91.27</td>
<td align="center">87.73</td>
<td align="center">84.16</td>
<td align="center">79.43</td>
</tr>
<tr>
<td align="center"><strong>9</strong></td>
<td align="center">91.52</td>
<td align="center">88.12</td>
<td align="center">84.54</td>
<td align="center">80.06</td>
</tr>
<tr>
<td align="center"><strong>10</strong></td>
<td align="center">91.68</td>
<td align="center">88.39</td>
<td align="center">84.81</td>
<td align="center">80.40</td>
</tr>
<tr>
<td align="center"><strong>11</strong></td>
<td align="center">91.79</td>
<td align="center">88.59</td>
<td align="center">85.08</td>
<td align="center">80.67</td>
</tr>
<tr>
<td align="center"><strong>12</strong></td>
<td align="center">91.80</td>
<td align="center">88.76</td>
<td align="center">85.30</td>
<td align="center">81.05</td>
</tr>
<tr>
<td align="center"><strong>13</strong></td>
<td align="center">91.90</td>
<td align="center">88.96</td>
<td align="center">85.45</td>
<td align="center">81.24</td>
</tr>
<tr>
<td align="center"><strong>14</strong></td>
<td align="center">91.94</td>
<td align="center">89.06</td>
<td align="center">85.62</td>
<td align="center">81.41</td>
</tr>
<tr>
<td align="center"><strong>15</strong></td>
<td align="center">91.97</td>
<td align="center">89.14</td>
<td align="center">85.79</td>
<td align="center">81.53</td>
</tr>
<tr>
<td align="center"><strong>16</strong></td>
<td align="center">92.06</td>
<td align="center">89.30</td>
<td align="center">85.84</td>
<td align="center">81.69</td>
</tr>
<tr>
<td align="center"><strong>17</strong></td>
<td align="center">92.12</td>
<td align="center">89.39</td>
<td align="center">86.02</td>
<td align="center">81.89</td>
</tr>
<tr>
<td align="center"><strong>18</strong></td>
<td align="center">92.10</td>
<td align="center">89.48</td>
<td align="center">86.03</td>
<td align="center">82.10</td>
</tr>
<tr>
<td align="center"><strong>19</strong></td>
<td align="center">92.18</td>
<td align="center">89.45</td>
<td align="center">86.10</td>
<td align="center">82.01</td>
</tr>
<tr>
<td align="center"><strong>20</strong></td>
<td align="center">92.15</td>
<td align="center">89.46</td>
<td align="center">86.13</td>
<td align="center">82.16</td>
</tr>
<tr>
<td align="center"><strong>21</strong></td>
<td align="center">92.20</td>
<td align="center">89.62</td>
<td align="center">86.26</td>
<td align="center">82.20</td>
</tr>
<tr>
<td align="center"><strong>22</strong></td>
<td align="center">92.16</td>
<td align="center">89.59</td>
<td align="center">86.27</td>
<td align="center">82.16</td>
</tr>
<tr>
<td align="center"><strong>23</strong></td>
<td align="center">92.22</td>
<td align="center">89.62</td>
<td align="center">86.29</td>
<td align="center">82.36</td>
</tr>
<tr>
<td align="center"><strong>24</strong></td>
<td align="center">92.22</td>
<td align="center">89.58</td>
<td align="center">86.30</td>
<td align="center">82.36</td>
</tr>
<tr>
<td align="center"><strong>25</strong></td>
<td align="center">92.24</td>
<td align="center">89.76</td>
<td align="center">86.42</td>
<td align="center">82.43</td>
</tr>
<tr>
<td align="center"><strong>26</strong></td>
<td align="center">92.20</td>
<td align="center">89.67</td>
<td align="center">86.35</td>
<td align="center">82.38</td>
</tr>
<tr>
<td align="center"><strong>27</strong></td>
<td align="center">92.21</td>
<td align="center">89.70</td>
<td align="center">86.38</td>
<td align="center">82.48</td>
</tr>
<tr>
<td align="center"><strong>28</strong></td>
<td align="center">92.28</td>
<td align="center">89.71</td>
<td align="center">86.35</td>
<td align="center">82.51</td>
</tr>
<tr>
<td align="center"><strong>29</strong></td>
<td align="center">92.25</td>
<td align="center">89.76</td>
<td align="center">86.40</td>
<td align="center">82.44</td>
</tr>
</tbody></table>
<p><img src="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1598175163552&di=6293d942b394e03ebcfd0ccd5e931831&imgtype=0&src=http://c.hiphotos.baidu.com/zhidao/pic/item/d31b0ef41bd5ad6eaf4a33b383cb39dbb6fd3c33.jpg"></p>

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